Nonlinear Blind Identification with Three-Dimensional Tensor Analysis

This paper deals with the analysis of a third-order tensor composed of a fourth-order output cumulants used for blind identification of a second-order Volterra-Hammerstein series. It is demonstrated that this nonlinear identification problem can be converted in a multivariable system with multiequat...

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Bibliographic Details
Published in:Mathematical Problems in Engineering 2012-01, Vol.2012 (2012), p.472-493-128
Main Authors: Cherif, I., Abid, S., Fnaiech, F.
Format: Article
Language:English
Subjects:
Algorithms
Blinds
Convergence
Identification
Least squares method
Mathematical analysis
Mathematical problems
Nonlinearity
Random variables
Signal processing
Signal to noise ratio
Tensor analysis
Tensors
Three dimensional
Three dimensional analysis
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Summary:This paper deals with the analysis of a third-order tensor composed of a fourth-order output cumulants used for blind identification of a second-order Volterra-Hammerstein series. It is demonstrated that this nonlinear identification problem can be converted in a multivariable system with multiequations having the form of Ax+By=c. The system may be solved using several methods. Simulation results with the Iterative Alternating Least Squares (IALS) algorithm provide good performances for different signal-to-noise ratio (SNR) levels. Convergence issues using the reversibility analysis of matrices A and B are addressed. Comparison results with other existing algorithms are carried out to show the efficiency of the proposed algorithm.
ISSN:1024-123X
1563-5147
DOI:10.1155/2012/284815